CN109583951B - Electric power market difference contract electric quantity decomposition technology and decomposition result comprehensive evaluation method - Google Patents
Electric power market difference contract electric quantity decomposition technology and decomposition result comprehensive evaluation method Download PDFInfo
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Abstract
The invention relates to an electric power market spread contract electric quantity decomposition technology and a decomposition result comprehensive evaluation method, comprising the following steps: decomposing annual contract electric quantity according to a corresponding annual typical output curve; decomposing the monthly decomposition electric quantity to the day by combining the day number of each type of day and the electric quantity in the month through annual to monthly decomposition and cross correction; according to the data, decomposing the solar contract electric quantity to each time period, and correcting the deviation of the decomposed electric quantity caused by the overhaul plan based on a cross correction algorithm to form a financial settlement and delivery curve of the authorized spread contract electric quantity of each time period; and finally, establishing a post-evaluation index system for contract electric quantity decomposition, and evaluating the decomposition result from four angles of risk, fairness, economy and rationality. The novel power market contract electric quantity decomposition method and the post-evaluation model can provide reasonable settlement basis for both power generation and purchase parties, evaluate the advantages and disadvantages of the contract electric quantity decomposition method and realize the coordinated operation of the contract market and the spot market.
Description
Technical Field
The invention relates to the field of power systems, in particular to a power market spread contract electric quantity decomposition technology and a decomposition result comprehensive evaluation method.
Background
Because the electric energy cannot be stored economically, the medium-and-long-term contract is equivalent to establishing a virtual inventory, and locking the price of the electric energy in advance so as to avoid the risk of fluctuation of the electric price caused by mismatching of supply and demand. Currently, in countries such as the united states, the united kingdom, australia, etc., where electric power marketization operations have been performed, a large amount of electric power is exchanged using medium-to-long-term contracts (physical contracts or financial contracts). The existing research shows that the introduction of the medium-and-long-term contracts is beneficial to reducing the behavior of power generation companies to control the market by utilizing the market force and the oligopolistic market force, so that the efficient market equilibrium electricity price is formed, and the market price is reduced.
In the middle-long term contract actual execution phase, the contract electricity quantity needs to be scientifically decomposed into each transaction period so as to perform physical execution or financial settlement. The contract electric quantity decomposition should fully consider the actual unit characteristics, the power grid operation condition and the load condition. The scientificity and rationality of the contract electric quantity decomposition directly influence the execution condition of the contract, and the scientific and reasonable contract decomposition has very important significance in the aspects of completing the contract, stabilizing the electricity price, avoiding the risk of the spot market and the safe and economic operation of the power grid.
The development of new electrical changes makes existing research difficult to adapt to the application requirements of new environments. Therefore, a new electric power market contract electric quantity decomposition method and a post-evaluation model are needed to be researched, a reasonable settlement basis is provided for both power generation and purchase parties, the advantages and disadvantages of the contract electric quantity decomposition method are evaluated, and the coordinated operation of the contract market and the spot market is realized.
Disclosure of Invention
The invention mainly solves the technical problem of providing an electric power market spread contract electric quantity decomposition technology and a decomposition result comprehensive evaluation method.
The invention adopts the following technical scheme:
analyzing the development profile of the electric power market, summarizing contract decomposition requirements of the electric power market under new electricity change, decomposing annual contract electric quantity of each power generation company according to a corresponding annual typical output curve, and obtaining ideal monthly decomposition electric quantity of each power generation company when a maintenance plan is not considered;
and obtaining a corrected monthly contract electric quantity decomposition matrix through annual to monthly decomposition and cross correction. Decomposing the monthly decomposition electric quantity of each power generation company into days according to the working days of each month, the days of double holidays and the load quantity of the corresponding type of days to obtain the daily decomposition electric quantity of each type of power generation company;
decomposing daily contract electric quantity of each power generation company to 48 points per day according to a daily typical load curve by taking the typical daily load curve as a basis to obtain decomposed electric quantity of each power generation company in each transaction period;
correcting the deviation of the decomposed electric quantity caused by the overhaul plan of the power generation company based on a cross correction algorithm to form a financial settlement and delivery curve of the electric quantity of government authorized spread contracts (namely base contracts) in each period;
and finally, establishing a post-evaluation index system for contract electric quantity decomposition, and evaluating the decomposition result from four angles of risk, fairness, economy and rationality.
The technical scheme provided by the invention has the beneficial effects that:
the electric quantity decomposition model constructed by the invention can fairly decompose the electric quantity of the government authorized spread contract, so that the proportion of the electric quantity to the total load is maintained within a certain fluctuation range, and the share of the market bid electric quantity can be well controlled. The novel power market contract electric quantity decomposition method and the post-evaluation model can provide reasonable settlement basis for both power generation and purchase parties, evaluate the advantages and disadvantages of the contract electric quantity decomposition method and realize the coordinated operation of the contract market and the spot market. The method can inhibit the risk of the spot market and is beneficial to the smooth transition of the electric power spot market.
Drawings
FIG. 1 is a government authorized spread contract resolution process;
FIG. 2 is a monthly electricity decomposition curve of each power generation company;
FIG. 3 is a contract exploded charge duty cycle;
FIG. 4 is a comprehensive evaluation result of monthly contract electricity quantity decomposition;
fig. 5 shows the results of the monthly evaluation of the four types of indexes.
Detailed Description
For better understanding of the objects, technical solutions and technical effects of the present invention, the present invention will be further explained below with reference to the accompanying drawings.
The invention provides a power market spread contract electric quantity decomposition technology and a comprehensive evaluation method of decomposition results, wherein the government authorized contract electric quantity decomposition is divided into three stages of adult month decomposition, month day decomposition and day period decomposition. Each phase contains 2 constraints, a total annual contract amount constraint corresponding to each power generation company, and a total contract amount constraint corresponding to each time period. Under the constraint condition, the maintenance plan of the power generation company is considered, and the final electric quantity decomposition matrix is obtained through adjustment. The implementation process comprises the following detailed steps:
the first step: annual month decomposition without consideration of planned maintenance
The new electricity changes the requirements set by the decomposition of the government authorized spread contract electricity quantity:
(1) The contract power decomposition plan should enable each power generation company to fully complete the government authorized spread contract power while ensuring that the government authorized spread contract power at each time interval is approximately equal in load.
(2) The main participation body of the electric power market comprises various types of coal power, water power, gas power, nuclear power units in provinces and power in plan and outside, and the types of the units are quite rich, so that the characteristics of different types of units need to be considered during decomposition, and the power generation benefit is improved through global optimization.
(3) When the contract electric quantity is decomposed, fairness of electric quantity distribution among the same type of units needs to be considered, and participation enthusiasm of market members is promoted.
(4) The decomposition result of government authorized spread contracts only forms financial delivery curves for settlement, not mandatory physical execution. The amount of deviation between the transaction plan formed by the current market clearing and the financial curve formed is settled according to the time-sharing electricity price of the current electric power market. The settlement period adopts 'Nianqing lunar junction'.
At the beginning of the year, when government authorized spread contracts are signed, a power generation company declares annual maintenance plans, upper and lower limits of power generation unit output, climbing speed, starting and stopping time and other unit operation parameters to a power grid company. And the power grid company combines the historical load data with the load prediction to obtain an annual load prediction curve. And buckling the non-bidding power supply part and the bilateral contract electric quantity on the load prediction curve to obtain a corrected annual load curve. Decomposing the sum of government authorized spread contract electric quantity signed by all power generation companies to 12 months according to the corrected annual load curve to obtain the government authorized contract total electric quantity of each month as follows
Wherein: q (Q) m Contract total electric quantity for government authorized m month, Q i Authorizing a spread contract amount of electricity for the annual government of power generation company i; l (L) m The method is characterized in that the method is used for generating power for m months in an annual typical power generation curve of a power generation unit, wherein the superscript w1 represents a coal-fired unit, the superscript w2 represents a gas unit, the W3 represents a hydroelectric unit, and the W4 represents a nuclear power unit.
And (5) respectively obtaining model year power generation curves of the thermal power unit, the gas unit, the hydroelectric unit and the nuclear power unit according to the historical data of the last 5 years. Decomposing annual contract electric quantity of each power generation company according to a corresponding model annual power generation curve to obtain ideal monthly decomposed electric quantity of each power generation company without considering maintenance plans
Wherein:
for ideal government authorized contract power for power generation company, month m, Q i Authorizing a spread contract amount of electricity for annual government of power generation company i, L m The method is characterized in that the method is used for generating power for m months in an annual typical power generation curve of a power generation unit, wherein the superscript w1 represents a coal-fired unit, the superscript w2 represents a gas unit, the W3 represents a hydroelectric unit, and the W4 represents a nuclear power unit.
And a second step of: cross correction algorithm considering maintenance plan
The ideal power decomposition scheme without considering the planned maintenance can be regarded as an N×12 power matrix [ Q ] (0) ] N×12 . The row vectors represent a decomposition scheme of contract electricity of each power generation company for 12 months, and the column vectors represent distribution of government authorized electricity of one period among N power generation companies. The annual contract amount of electricity constraint of the power generation company, and the contract amount of electricity constraint corresponding to each period can be expressed as
Wherein: q (Q) i,m Authorizing contract electric quantity for government of power generation company i month m, Q i Authorizing a spread contract amount of electricity for annual government of power generation company i, Q m Contract total power is authorized for the government for m months.
Corresponding to the 2 constraints, the cross correction algorithm comprises a transverse correction and a longitudinal correction. The correction of the power generation amount of each period of the ith power generation company is referred to as a lateral correction, and the correction of the power generation amount of each power generation company of the mth month is referred to as a vertical correction. The deviation amount can be continuously reduced by transverse and longitudinal correction until the load balance constraint and the contract electric quantity constraint are met. The specific steps of the cross correction algorithm considering overhaul are as follows:
step 1: solving ideal electric quantity decomposition scheme matrix [ Q ] (0) ] N×12 。
Step 2: and according to the annual contract electric quantity equation constraint condition, the maintenance condition is considered, and the transverse correction is carried out.
Let the number of days of maintenance of the power generation company i in the mth month be D i,m The overhaul capacity is G i,m The month power generation unbalance amount generated by the power generation company i in the mth month due to the overhaul can be obtained according to the ideal electric quantity decomposition result
Furthermore, the unbalance amount of the power generation company i in the whole year can be solved>
/>
Wherein: ΔQ i,m (0) D, decomposing the deviation amount for the month of the power generation company i in the mth month i,m For the number of days of overhaul of the power generation company i in the mth month, G i,m For the maintenance capacity of the power generation company i in the mth month, deltaQ i (0) The annual decomposition deviation amount of the power generation company i is obtained.
According to the proportion of the actual power generation amount of each power generation unit to the total power generation amount after the overhaul, the unbalanced amount of annual power generation amount is sharedBy each month, a lateral correction was performed. Generating capacity of power generation company i in mth month after transverse correction
Is that
Wherein:
for the government authorized contract amount of the power generation company i at the mth month after the transverse correction, +.>
For ideally the government authorized contract power for month m of power generation company, Δq i,m (0) The deviation amount is decomposed for the month of the power generation company i at the mth month.
Step 3: and carrying out longitudinal correction according to the constraint condition of the load balancing equation.
Let m 0 =1, assuming from initial period to mth 0 The 1 month contract quantity decomposition has been completed, i.e. the annual contract quantity equation constraint condition is satisfied, and the month contract quantity total constraint is also satisfied, and then only the mth needs to be considered 0 Contract electricity quantity decomposition for months and later. According to the moon load balance equation condition 0 The power generation amount of each power generation company in month is longitudinally corrected to obtain the m-th power generation company in the ith power generation company 0 Generating capacity of month
Is that
Wherein:
for longitudinal correction of the hairElectric company i at mth 0 Month of month decomposing electricity, +.>
For the power generation company i after transverse correction at the mth 0 Monthly government authorized contract electricity quantity,/-)>
Is the mth 0 Month resolvable cardinal contracts total power.
After each longitudinal correction, the constraint condition of the monthly load balance equation can be satisfied, but the constraint condition of the annual contract electric quantity equation of each unit is not satisfied, so that the transverse correction is needed again. At the moment, only the subsequent time interval is laterally corrected to ensure the mth 0 The amount of decomposed electricity of month and the preceding month can satisfy the constraint.
Step 4: and carrying out transverse correction to the subsequent time period according to the constraint condition of the annual contract electric quantity equation.
At t 0 After the time period is longitudinally corrected, the annual total power generation amount of each unit is unbalanced
Is that
Wherein:
for the annual decomposition deviation electric quantity of the power generation company i after longitudinal correction, Q i Authorizing a spread contract amount for the annual government of the power generation company i, +.>
The power generation company i decomposes the electric quantity for the month of the mth month after the vertical correction.
Unbalanced annual total power generation amount of each unit
According to the m-th of the generator set after longitudinal correction 0 The actual power generation amount of +1 to 12 months is distributed in proportion to the total power generation amount, namely +.>
Wherein:
for the government authorized contract amount of the power generation company i at the mth month after the transverse correction, +.>
For decomposing the electricity quantity of the power generation company i at the month of the mth after the vertical correction, +.>
The power generation company i after the vertical correction is decomposed into the power deviation by the year.
After each transverse correction, the method not only can meet the requirement of the mth 0 Month and previous month contract electric quantity constraint can also meet the annual contract total electric quantity equality constraint of each power generation company.
Step 5: let m 0 ＝m 0 +1, jump to step 3.
And carrying out cross correction on each month after the 5 steps. Finally, a corrected monthly contract electric quantity decomposition matrix Q can be obtained] N×12 。
The cross correction algorithm is not only suitable for annual month decomposition of contract electric quantity in consideration of planned maintenance, but also suitable for decomposition of other time periods and rolling correction in the execution process. The time period of the cross correction is changed from 12 months to other periods T, the transverse and longitudinal correction is carried out according to the flow, and finally, the decomposition scheme of the contract electric quantity can always be ensured to meet the constraint.
And a third step of: month-to-day decomposition of contract electricity
Decomposition and second stepAfter the crossover correction, the corrected monthly contract electricity quantity decomposition matrix Q is obtained] N×12 . And decomposing the monthly decomposition electric quantity of each power generation company into days according to the working days of each month, the days of double holidays and the loading quantity of the corresponding type of days to obtain the daily decomposition electric quantity of each type of power generation company. Assume that the load electric quantity on the d-th type day of the m month is Q m,d Days N m,d The day-resolved power of the power generation company i on the day of the d-type of the mth month is
In which Q is i,m,d Decomposing the electric quantity for the degree of the day of the type d day of the m month of the power generation company i; q (Q) i,m Decomposing electric quantity for the month of the power generation company i in the mth month; q (Q) m,d Load electric quantity of the type d day of the mth month; n (N) m,d Day of type d day of month m.
Fourth step: within-day transaction period decomposition of contract electricity quantity
And extracting daily typical load curves of users on holidays, holidays and workdays according to the historical data, decomposing daily contract electric quantity of each power generation company to 48 points per day according to the daily typical load curves, and obtaining the generated energy of each power generation company in each transaction period. Assuming that the load of the mth trading period of the day of m months d is Q according to the daily typical load curve m,d (t) the decomposed electric quantity of the power generation company i in the period is
In which Q is i,m,d (t) decomposing the electric quantity for the contract of the power generation company i in the t period of the d-type day of the mth month, Q i,m,d Decomposing the total power for the contract of the power generation company i on the d-type day of the mth month, Q m,d (t) contract decomposition total electric quantity for the t period of the d-type day of the mth month, Q m,d (t) decomposing the total power for the contract on the d-type day of the mth month.
After the split-up period, the contracted split power has been expressed in the form of power. By this time, a government authorized spread contract power delivery curve for the power generation company i has been formed throughout the year. And at the beginning of each month, adding the monthly bidding generating capacity to the delivery curve, and performing processing check and system safety check through a whole factory technology to form an executable scheduling curve of each month. The above decomposition result is merely used as a basis for financial settlement, and in the actual execution stage, the actual power generation amount per month, day, and period may not be completely executed according to the decomposition result. The part of the power generation company with more power generation is sold at the market price of electricity clearing, and the part with insufficient power generation also needs to buy corresponding electric quantity from the market to supplement the shortage.
The decomposition method considers the difference of unit types when initially forming an ideal power generation matrix and considers the influence of planned overhaul of a power generation company. On the basis, the method of proportional decomposition is adopted as much as possible, so that the proportion of the contract electric quantity of each period obtained by decomposition to the total load of each period is as consistent as possible, the proportion of the decomposition electric quantity of each power generation company to the total contract electric quantity of each power generation company is as consistent as possible, and the fairness of decomposition is realized to the greatest extent.
When carrying out analysis and research on contract decomposition results, a comprehensive and reasonable contract decomposition result evaluation index system is established, and a proper evaluation method is selected to evaluate the contract decomposition results.
In the process of evaluating the contract electric quantity decomposition result, 7 indexes are selected from four aspects of risk, fairness, economy and rationality to establish a comprehensive evaluation index system, as shown in table 1.
TABLE 1 contract resolution evaluation index System
1. Risk index
(1) Base electricity quantity duty ratio average value
Let delta be i The base power duty cycle for the t-th period,
the average value of the load proportion of the base electric quantity of the T time periods is obtained. Then there are:
wherein: delta t The base power of the t-th period is the load proportion value,
for the average value of the load proportion of the base electric quantity of T time periods, Q it Decomposing electric quantity for contract of ith power generation company in the t period, Q t Is the total load capacity of the t-th period.
(2) Standard deviation of load ratio of base electric quantity
Setting epsilon as the standard deviation of the load proportion of the basic electric quantity
Wherein: epsilon is the standard deviation of the load proportion of the electricity quantity with the base number, delta i And the electricity quantity is the basic number electricity quantity duty ratio of the t period, and N is the number of power generation companies.
In the electricity market, fluctuations in electricity prices are the greatest risk faced by market members. The existing research shows that the introduction of the long-term contract is beneficial to reducing the market price of electricity, meanwhile, the holding capacity of the power generation company can be controlled, the market force of the power generation company is restrained, and the proper contract electric quantity ratio can improve the stability of the market.
The average of the base charge to load charge ratio reflects the government control level over the market bid space. In the process of power marketing construction, the openness of the market is generally regulated by provincial power market planning construction departments or superior departments, and the signing of the total electric quantity of government authorized contracts is also based on the openness of the market. After the contract is decomposed, the closer the duty ratio of the base electricity quantity is to a preset specified value, the more reasonable the risk is controlled.
The standard deviation of the ratio of the base electricity quantity to the load electricity quantity reflects the fluctuation degree of the market bidding space. Scientific and reasonable contract decomposition methods need to make market bidding spaces of all time periods equivalent. The smaller the standard deviation, the more stable the level of the market bid space.
2. Fairness index
(1) Load factor correlation coefficient between different power generation companies
Let gamma be it The load factor of the power generation company i in the t period is obtained. Gamma ray i The mathematical expression is as follows for the vector consisting of the load factors of each period in one month of the ith power generation company:
γ i ＝[γ i1 ,γ i2 ,...,γ iT ]
wherein: gamma ray it For the load factor of the power generation company i in the t period, Q it Decomposing electric quantity for contract of ith power generation company in the t period, G i For the ith power generation company to have the maximum power generation capacity in the t period, gamma i And i, a load factor vector of a power generation company.
The mathematical expression of the load factor correlation coefficients of the power generation company i and the power generation company j is:
wherein: gamma ray i For the power generation company i load factor vector, gamma j And the load factor vector is the power generation company j.
(2) Standard deviation of contract completion progress of different power generation companies
Let definition k ij For the progress coefficient of the power generation company i at the end of the previous j months, the effective power generation time utilization rate of the ith power generation company is represented by the mathematical expression:
wherein: t is the number of time units (for example, when the annual electric quantity is decomposed to month, T is 12), Q it For the generated energy of the ith power generation company in the time unit t, Q i Annual contract electric quantity for ith power generation company, W it The maximum network power of the power generation company i in the time unit t is obtained.
The fairness index described above takes into account fairness of contract electricity split among different power generation companies. The annual utilization hours of units of different power generation companies are different, the total electric quantity of distributed government authorized spread contracts is also different, the load rates of the government authorized spread contracts are not at the same level, and the government authorized spread contracts cannot be directly compared. The consistency of contract electric quantity decomposition curve fluctuation of different power generation companies is reflected by using the load rate correlation coefficient, and whether the decomposition method is fair to any 2 power generation companies can be well evaluated.
In order to ensure fairness, the contract completion progress of each power generation company is generally kept as uniform as possible on any one time section. However, different power generation companies have different maintenance plans in one year, and it is not reasonable to simply measure the completion progress of the contract decomposed electric quantity, so that the concept of a progress coefficient is introduced. Progress coefficient k ij The meaning of (2) is: and in the first j months, the ratio of the maximum power generation capacity utilization rate of the power plant to the annual maximum power generation capacity utilization rate of the power plant represents the completion progress of the contract decomposition electric quantity of the power generation company relative to the whole year on a certain time section. k (k) ij More than 1, the maximum power generation capacity utilization rate of the power generation company at and before j months is higher than the average value of the whole year, and the contract electric quantity completion progress is relatively advanced; k (k) ij And < 1, indicating that the contract electric quantity completion progress of the power generation company is relatively lagged. Therefore, each power generation company needs to be operated at k in each period ij =1 is the goal as close as possible.
3. Economic index
(1) Whole network load factor weighted average
Defined gamma it The load factor of the power generation company i in the t period is obtained. Let gamma be t The weighted average of the load rates of all power generation companies in the t-th period is expressed as follows:
wherein: gamma ray t Is the weighted average value gamma of the load rates of all power generation companies in the t-th period it For the load factor of the power generation company i in the t period, Q it Generating capacity which is decomposed in a time unit t for an ith generating company; g i Maximum power generation capacity of the ith power generation company in the t period, Q i Is the annual contract electricity quantity of the ith power generation company.
The weighted average value of the load rate of the whole network reflects the overall utilization rate of the capacity of the power generation equipment, and the economic benefit is analyzed from the aspect of investment utilization rate.
In addition, after the actual market test run, the benefits of the power generation company and the power grid company can be calculated through indexes such as real-time electricity price, network loss, contract completion deviation micro-increment cost and the like, and the economy of the decomposition method is reflected. Most of the indexes need evidence analysis and can be added into an index system after conditions are met.
4. Rationality index
(1) Base power and load correlation coefficient
Set Q it Decomposing electric quantity for contract of ith power generation company in the t period, L t And as a load curve, obtaining the correlation coefficient of the basic electric quantity and the load, wherein the mathematical expression is as follows:
wherein: r is (r) iL For the i-th power generation company and the load, Q it Decomposing electric quantity for contract of ith power generation company in the t period, L t Is a load curve.
(2) Whole network progress coefficient average value
Defined k ij For the progress coefficient of the power generation company i at the end of the previous j months, the mathematical expression of the average value of the whole network progress coefficient is:
wherein:
is the average value k of the progress coefficient of the whole network ij Is the progress factor of the power generation company i at the end of the previous j months.
The above-mentioned rationality index reflects the scientificity and scheduling executability of the contract power resolution scheme. The larger the base electric quantity and load correlation coefficient is, the more consistent the base electric quantity curve and load fluctuation are, which is favorable for dispatching execution and stabilizes spot market fluctuation. The average value of the whole network progress coefficient represents the degree of lead or lag of the completion degree of the whole network base power, and the closer the value is to 1, the more reasonable the decomposition of the base power is represented, and the higher the performability is.
Often, the object of evaluation has a plurality of index attributes, which differ in dimension and variation range, and in order to analyze and compare under the same evaluation system, it is necessary to perform normalization processing on data.
The invention adopts a range normalization method. First, the risk index, the economic index, the fairness index, and the rationality index are classified into a forward index, a reverse index, and an interval index 3.
For the forward index, the larger the index value is, the better the index value is, and the standardized formula is as follows:
for the reverse index, the smaller the index value is, the better the index value is, and the standardized formula is as follows:
for the interval index, the closer the index value is to a certain interval, the better, and the standardized formula is:
wherein: x's' ij The index value after normalization processing; x is x ij Is an actual index value; v 1j 、v 2j 、v 3j 、v 4j The critical point for the standardized value change corresponding to the interval index can be selected according to the actual condition of the interval index.
When determining the index weight, not only the subjective experience of an expert is required to be considered, but also objective differences among all index data are required to be fully considered, and deviation caused by the subjective weight determination is avoided as much as possible. Therefore, the invention utilizes a subjective and objective weight calculation method combining an analytic hierarchy process and an entropy weight method to determine the weight of each index selected.
1. The subjective weight determination by the analytic hierarchy process is as follows:
1) Constructing pairwise comparison judgment matrix
In the analytic hierarchy process, a method of comparing indexes pairwise to establish a pair comparison matrix is adopted. I.e. two indices x are taken at a time i And x j In a, a ij Represents x i And x j The ratio of the influence on the evaluation target is calculated by using a matrix A= [ a ] for all comparison results ij ] n×n The expression "a" refers to a pairwise comparison judgment matrix (judgment matrix for short). It can be easily seen that if x i And x j The ratio of the influence on the evaluation target is a ij X is then j And x i The ratio of the influence on the evaluation target should be 1/a ij 。
2) Hierarchical single ordering
Judgment matrix A corresponds to maximum eigenvalue lambda max Feature vectors of (a)And W, normalizing to obtain the weight of the relative importance of the corresponding index of the layer to the upper layer target.
3) Consistency check
Calculating a consistency index CI:
calculating a consistency ratio CR:
wherein: CI is an inconsistency index of the paired comparison matrix A; lambda (lambda) max Comparing the maximum eigenvalues of the matrix in pairs; RI is an average random consistency index and is only determined by the order n of the pair comparison matrix A; CR is the ratio of the degree of inconsistency of the pair comparison matrix A, and when CR is smaller than 0.10, the consistency of the judgment matrix is considered to be acceptable, otherwise, the judgment matrix is appropriately corrected; if the consistency test is passed, obtaining the maximum eigenvector of the paired comparison matrix, and then carrying out normalization processing, thereby obtaining the subjective weight vector of the evaluation index.
Wherein: w (w) s Is composed of
The column vector is the maximum eigenvector of the pair-wise comparison matrix A, W s Subjective weight vectors are used for evaluating the index.
2. The step of determining objective weight by the entropy weight method is as follows:
the entropy weight method is an objective weight giving method and can be the mostThe data of the index system is utilized to a large extent to calculate each index weight value. For a certain index j, if the index value x of the sample to be evaluated ij The larger the gap between the two indexes is, the smaller the entropy value of the index is, and according to the information entropy theory, the larger the information quantity provided by the index is, the larger the influence on the comprehensive evaluation result is, and the higher the weight is.
1) Calculating the characteristic specific gravity of the ith sample under the jth index
Wherein: p is p ij For the characteristic specific gravity of the ith project under the jth index, x' ij Is the index value after normalization processing.
2) Calculation of entropy values
Wherein: h j Entropy value of j-th evaluation index, p ij Is the characteristic specific gravity of the ith project under the jth index. Wherein when p ij When=0, take p ij lnp ij ＝0。
3) Calculation of weights
Wherein:
objective weights for the evaluation indexes; h j The entropy value of the j-th evaluation index.
After expert experience decision, the subjective weight of each index to the evaluation target is obtained
The objective weight obtained by the entropy weight method is +.>
Adopting a multiplication integration method in the comprehensive integration weighting method to obtain the comprehensive weight omega considering the subjective weight and the objective weight j 。
Wherein: omega j For the comprehensive weight of each evaluation index,
subjective weight for each evaluation index +.>
Objective weights for the evaluation indexes;
for further understanding of the present invention, practical application of the present invention will be explained taking government authorized spread contract power resolution in 2017 in the Zhejiang grid section as an example. In order to simplify analysis and emphasize algorithm principles, the method only considers the situation of one plant and one machine, 10 units are selected for analysis, wherein G1-G5 are coal-fired units, G6-G7 are gas units, G8-G9 are hydroelectric units, and G10 is a nuclear power plant. Contract data and genset parameters are shown in Table 3, and annual genset service plans are shown in Table 4.
TABLE 3 contract data and Unit parameters for Power generating companies
Table 4 annual service plan for power generation company
Decomposing the annual government authorized spread contract electric quantity of the power generation company to each month by adopting a government authorized spread contract decomposition model, taking the scheduled maintenance of the unit into consideration, and correcting the decomposed electric quantity by adopting a cross correction algorithm to obtain month decomposition curves before and after correction, as shown in figure 2. As can be seen from fig. 2, in an ideal situation without considering the maintenance schedule, the units of the same type have similar decomposition curves, which represents the fairness of decomposition. The load rate of different types of units in one year fluctuates, the output of the hydroelectric units reaches a peak when the load of the hydroelectric units is high in the peak of Shui Ji, the output of the gas units reaches a peak when the load of the gas units is highest in summer, the output of the nuclear power units in the whole year tends to be stable, and the difference between the unit characteristics is reflected.
As can be seen from comparing fig. 2 (a) with fig. 2 (b), after considering the maintenance schedule, the power generation company's power decomposition curve is corrected, and its monthly power decomposition is also adjusted according to the power generation capacity. The power generation company 1 overhauls in 8 months, the month decomposition electric quantity of 8 months is correspondingly reduced after correction, and meanwhile, the month decomposition electric quantity of the rest months is correspondingly reduced and increased in proportion along with the change of the power utilization level of the whole network.
Taking 4 months in 2017 as an example, decomposing the monthly decomposition electric quantity of the power generation company to the daily degree according to the daily type to obtain a 4-month daily degree decomposition curve. And decomposing the daily degree decomposition electric quantity of a working day of 4 months to each transaction period to obtain an electric quantity decomposition curve of the day. After summarizing, the actual load electric quantity is compared, and the result is shown in figure 3. As can be seen from fig. 3, the daily charge decomposition curve formed by decomposition according to the characteristic of the day type and the daily charge decomposition curve formed based on the typical daily load curve are substantially consistent with the trend of the load curve. The contract decomposition electric quantity in two stages has basically stable proportion, floats in an acceptable range, basically accords with the expected risk control force, can meet the load demand of each period, and achieves the effects of considering benefits of all parties and moderately introducing market competition.
And obtaining comprehensive weights of all indexes by using a subjective and objective combined weighting method, thereby obtaining comprehensive evaluation results of the power decomposition of contracts in each month, as shown in figure 4. Meanwhile, the power decomposition result of contracts in each month is evaluated in terms of risk, economy, rationality and fairness, as shown in fig. 5.
As can be seen from fig. 4, after the application of the contract decomposition model, the evaluation at each month is different, and the rising trend is generally presented. The two reasons are that the completion progress coefficient of the power generation company gradually approaches to the average progress of the whole network along with the advancement of time, and finally the contract electric quantity is completed at full capacity; and secondly, the electric quantity cross correction of the spread contract is corrected to the subsequent month, the correction times obtained in the subsequent month are more, and the correction deviation amount is smaller. In engineering practice, the deviation of contract electric quantity decomposition is gradually reduced, which is also beneficial to improving the scheduling executable. As can be seen from the figure 5, the contract decomposition model is reasonable in decomposition throughout the year, meets the requirement of fairness, effectively controls market risks, and has relatively poor evaluation results of economic indexes.
Overall, the above evaluation results evaluate to some extent the merits of the decomposition results of the spread contract decomposition model.
Claims (4)
1. The utility model provides an electric power market spread contract electric quantity decomposition technology and a decomposition result comprehensive evaluation method, which is characterized in that the method comprises the following steps:
1) Decomposing annual contract electric quantity of each power generation company according to a corresponding annual typical output curve based on actual requirements of an electric power market to obtain ideal monthly decomposed electric quantity of each power generation company when a maintenance plan is not considered; obtaining a corrected monthly contract electric quantity decomposition matrix through ideal monthly decomposition electric quantity and cross correction;
the step 1) specifically comprises the following steps:
when the initial annual base contracts are signed, a power generation company declares an annual maintenance plan and the operating parameters of a power generation unit to a system dispatching mechanism, wherein the operating parameters of the power generation unit comprise upper and lower output limits, climbing and landslide rates and starting and stopping time; the system scheduling mechanism obtains a annual load prediction curve according to the historical load data and the load prediction, and deducts a load curve corresponding to partial electric quantity which does not participate in the market from the load prediction curve to obtain a corrected annual load curve;
respectively obtaining annual typical output curves of a thermal power unit, a gas unit, a hydroelectric unit and a nuclear power unit from historical data of the last 5 years; then, decomposing the annual contract electric quantity of each power generation company according to the corresponding annual typical output curve to obtain ideal monthly decomposed electric quantity of each power generation company when the maintenance plan is not considered:
in which Q is i,m Decomposing electric quantity for the month of the power generation company i in the mth month; q (Q) i Contract power for annual base for power generation company i;
the method is characterized in that the method is used for generating power in the m month in an annual typical generating curve of a generating set, wherein the superscript w1 represents a coal-fired unit, the superscript w2 represents a gas unit, the W3 represents a hydroelectric unit, and the W4 represents a nuclear power unit;
after an ideal monthly electricity decomposing scheme is obtained, the electricity decomposing scheme is corrected according to an annual overhaul plan reported by a power generation company by combining with a cross correction algorithm, and finally the monthly electricity decomposing scheme considering the overhaul plan is obtained;
the method comprises the steps of correcting an electric quantity decomposition scheme according to an annual overhaul plan reported by a power generation company, and finally obtaining a monthly electric quantity decomposition scheme considering the overhaul plan, and specifically comprises the following steps:
the cross correction algorithm comprises transverse correction and longitudinal correction; the correction of the generated energy of each period of the ith power generation company according to the annual contract electric quantity constraint is called transverse correction; the correction of the generated energy of all power generation companies in a certain period according to the whole network load balance constraint is called longitudinal correction; the cross correction algorithm can be applied at each stage of electric quantity decomposition, and the deviation amount can be continuously reduced on the premise of meeting constraint through cross correction and longitudinal cross correction, so as to correct the electric quantity decomposition scheme;
the constraints and processes corresponding to the lateral corrections can be expressed as:
wherein: q (Q) i,m Decomposing electric quantity for the month of the power generation company i in the mth month; q (Q) i Contract power for annual base for power generation company i; ΔQ i (0) The annual decomposition deviation amount of the power generation company i; ΔQ i,m (0) Decomposing the deviation amount for the month of the power generation company i in the mth month;
decomposing electric quantity for the month of the power generation company i in the mth month after the transverse correction; />
Decomposing electric quantity for the month of the power generation company i in the mth month before transverse correction;
the constraints and processes corresponding to the longitudinal corrections can be expressed as
Wherein: q (Q) i,m Decomposing electric quantity for the month of the power generation company i in the mth month; q (Q) m Total power for a base contract that is resolvable at month m;
for the power generation company i after transverse correction at the mth 0 Month decomposition electric quantity of month; />
Is the mth 0 Month resolvable cardinal contracts total power; />
For generating company i after longitudinal correction at mth 0 Month decomposition electric quantity of month;
in the annual to monthly decomposition process of contract electric quantity, the obtained ideal electric quantity decomposition scheme without considering the planned maintenance can be regarded as an N multiplied by 12 electric quantity matrix [ Q ] (0) ] N×12 The method comprises the steps of carrying out a first treatment on the surface of the Taking the electric quantity matrix as a basis, considering the planned maintenance of a power generation company, and performing cross correction; the method comprises the following specific steps:
1.1 Obtaining ideal electric quantity decomposition scheme matrix Q (0) ] N×12 ；
1.2 According to annual contract electric quantity equation constraint, the monthly decomposition deviation amount generated by the overhaul plan is distributed to the whole year for transverse correction;
1.3 Equal proportion adjusting mth according to the confinement of the monthly load balance equation 0 Performing longitudinal correction on the monthly decomposition electric quantity of all power generation companies in months;
1.4 Calculating the annual decomposition deviation amount caused by longitudinal correction, and spreading the annual decomposition deviation amount to the subsequent month for transverse correction;
1.5 Let m 0 ＝m 0 +1, jump to step 1.3);
through the 5 steps, each month is subjected to cross correction sequentially and backwards, and finally the month contract electric quantity decomposition matrix [ Q ] considering the maintenance plan can be obtained] N×12 The annual to monthly decomposition of the contract electric quantity is completed;
2) According to the days of each type of day and the electricity consumption in the month, decomposing the monthly decomposition electricity further into the days;
3) Decomposing daily contract electric quantity of each power generation company to 48 points per day according to a daily typical load curve by taking the typical daily load curve as a basis, namely, decomposing electric quantity of each power generation company in each transaction period by using one point of half an hour;
4) Correcting the deviation of the decomposed electric quantity caused by the overhaul plan of the power generation company based on a cross correction algorithm to form a financial settlement delivery curve of the base contract electric quantity of each period;
5) And establishing a post-evaluation index system of the contract electric quantity decomposition result, and evaluating the decomposition result from four angles of risk, fairness, economy and rationality.
2. The power market spread contract electricity quantity decomposition technique and decomposition result comprehensive evaluation method according to claim 1, wherein the step 2) specifically includes:
the corrected monthly contract electricity quantity decomposition matrix [ Q ] is obtained through annual to monthly decomposition and cross correction] N×12 The method comprises the steps of carrying out a first treatment on the surface of the Decomposing the monthly decomposition electric quantity of each power generation company into days according to the working days of each month, the days of double holidays and the load quantity of the corresponding type of days to obtain the daily decomposition electric quantity of each type of power generation company:
in which Q is i,m,d Decomposing the electric quantity for the degree of the day of the type d day of the m month of the power generation company i; q (Q) i,m Decomposing electric quantity for the month of the power generation company i in the mth month; q (Q) m,d Load electric quantity of the type d day of the mth month; n (N) m,d Day of type d day of month m.
3. The power market spread contract electricity quantity decomposition technique and decomposition result comprehensive evaluation method according to claim 1, wherein the step 3) specifically includes:
respectively extracting typical daily load curves of the system on holidays, holidays and workdays from the historical load data; decomposing daily contract electric quantity of each power generation company to 48 points per day according to a daily typical load curve by taking the typical daily load curve as a basis to obtain decomposed electric quantity of each power generation company in each transaction period, and forming an electric quantity decomposition curve for financial settlement;
when an ideal decomposition matrix is formed initially, the decomposition method considers the difference of unit types and considers the influence of planned overhaul of a power generation company; in addition, the proportion of the contract electric quantity of each period obtained by decomposition to the total load of each period is as consistent as possible by the proportion decomposition method, and the proportion of the decomposition electric quantity of each power generation company to the total contract electric quantity of each power generation company is as consistent as possible, so that the fairness of decomposition is realized to the greatest extent.
4. The power market spread contract electricity quantity decomposition technique and decomposition result comprehensive evaluation method according to claim 1, wherein the step 5) specifically comprises:
firstly, corresponding indexes are extracted from four aspects of risk, fairness, economy and rationality according to an electric quantity decomposition curve and a load curve, and an evaluation index system is established; the risk index comprises a base electric quantity duty ratio average value and a base electric quantity duty ratio standard deviation; the fairness index comprises the standard deviation of the load rate correlation coefficient among different power generation companies and the contract completion progress of different power generation companies; the economic index comprises a weighted average value of the load rate of the whole network; the average value of the rationality index base electricity quantity, the load related coefficient and the whole network progress coefficient;
then, carrying out standardization processing on the sample data by using a range normalization method; firstly, classifying the risk index, the economical efficiency index, the fairness index and the rationality index into 3 classes of forward indexes, reverse indexes and interval indexes;
for the forward index, the larger the index value is, the better the index value is, and the standardized formula is as follows:
for the reverse index, the smaller the index value is, the better the index value is, and the standardized formula is as follows:
for the interval index, the closer the index value is to a certain interval, the better, and the standardized formula is:
wherein: x is x i ′ j The index value after normalization processing; x is x ij Is an actual index value; v 1j 、v 2j 、v 3j 、v 4j The critical point for the change of the standardized value corresponding to the interval index can be selected according to the actual condition of the interval index;
when determining the index weight, not only the subjective experience of an expert is required to be considered, but also the objective difference among all index data is required to be fully considered, so that the deviation caused by the subjective weight determination is avoided as much as possible; determining the weight of each selected index by using a subjective and objective weight calculation method combining an analytic hierarchy process and an entropy weight method; the combination of these two methods has the following advantages: reducing subjectivity of the assessment; the standard is reasonable and effective, and the fitting is practical; the purpose of evaluating the effect of the spread contract decomposition method is to provide a reference for the selection of the spread contract decomposition method;
the subjective weight determination by the analytic hierarchy process is as follows:
step 1: constructing pairwise comparison judgment matrix
In the analytic hierarchy process, a method of performing pairwise comparison on indexes to establish a pairwise comparison matrix is adopted, and two indexes x are taken each time i And x j In a, a ij Represents x i And x j The ratio of the influence on the evaluation target is calculated by using a matrix A= [ a ] for all comparison results ij ] n×n The expression, A is called a pairwise comparison judgment matrix; if x i And x j The ratio of the influence on the evaluation target is a ij X is then j And x i The ratio of the influence on the evaluation target should be 1/a ij ；
Step 2: hierarchical single ordering
Judgment matrix A corresponds to maximum eigenvalue lambda max Feature vectors of (a)W, after normalization, the weight of the relative importance of the corresponding index of the layer to the upper layer target is obtained;
step 3: consistency check
Calculating a consistency index CI:
calculating a consistency ratio CR:
wherein: CI is an inconsistency index of the paired comparison matrix A; lambda (lambda) max Comparing the maximum eigenvalues of the matrix in pairs; RI is an average random consistency index and is only determined by the order n of the pair comparison matrix A; CR is the ratio of the degree of inconsistency of the pair comparison matrix A, and when CR is smaller than 0.10, the consistency of the judgment matrix is considered to be acceptable, otherwise, the judgment matrix is appropriately corrected; if the consistency test is passed, obtaining the maximum eigenvector of the paired comparison matrix, and then carrying out normalization processing, thereby obtaining the subjective weight vector of the evaluation index;
wherein: w (w) s Is composed of
The column vector is the maximum eigenvector of the pair-wise comparison matrix A, W s Subjective weight vectors for the evaluation index;
the entropy weight method is an objective weight giving method, so that objective weights of all indexes can be determined by utilizing the data of the evaluation index system; according to the characteristics of entropy, not only the randomness and the disorder degree of an event can be judged through the entropy value, but also the discrete degree of a certain index can be judged, namely, the smaller the entropy value is, the larger the discrete degree is, the more information quantity carried by the index is, and the larger the influence on comprehensive evaluation is, so that the higher weight is given to the index;
the calculation step of the entropy weight method for determining the objective weight is as follows:
step 1: feature specific gravity calculation
Wherein: p is p ij To the characteristic specific gravity of the ith project under the jth index, x i ′ j The index value after normalization processing;
step 2: evaluation index entropy calculation
Wherein: h j Entropy value of j-th evaluation index, p ij The characteristic specific gravity of the ith project under the jth index; wherein when p ij When=0, take p ij lnp ij ＝0；
Step 3: evaluation index weight calculation
Wherein:
objective weights for the evaluation indexes; h j Entropy value of j-th evaluation index;
subjective weight vector of each index is obtained
And objective weight vector +.>
Then, a multiplication integration method in the comprehensive integration weighting method is adopted to obtain the comprehensive weight omega considering the subjective weight and the objective weight j ；
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